Improving remote sensing estimation of Secchi disk depth for global lakes and reservoirs using machine learning methods

Secchi disk depth (SDD) is a simple but particularly important indicator for characterizing the overall water quality status and assessing the long-term dynamics of water quality for diverse global waters. For this reason, countless efforts have been made to collect SDD data from the field and through remote sensing systems. Many empirical and semianalytical algorithms have been proposed to estimate SDD from different satellite images for a specific or regional water. However, the construction of a robust global SDD estimation model is still challenging due to the nonlinear response of SDD to optical properties and the complex physical and biogeochemical processes of different waters. Therefore, machine learning methods to better interpret nonlinear processes were used to improve remote sensing estimations of SDD for global lakes and reservoirs based on a global matchup dataset from Landsat TM (N = 4099), ETM+ (N = 2420), and OLI (N = 1249) covering in situ SDD from 0.01 m to over 18 m. Overall, extreme gradient boosting (XGBoost) and random forest (RF) had better SDD retrievals than back propagation neural network, support vector regression, empirical and quasi-analytical models showing high precision with mean relative error of approximately 30% and good agreements with the long-term in situ SDD in different waters with various optical properties. Our results can support long-term global-level water quality evaluation and thus making informed decisions about development policy.